A binning approach to quickest change detection with unknown post-change distribution
The problem of quickest detection of a change in distribution is considered under the assumption that the prechange distribution is known, and the postchange distribution is only known to belong to a family of distributions distinguishable from a discretized version of the prechange distribution. A...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/137146 |
| _version_ | 1826121202946539520 |
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| author | Lau, Tze Siong Tay, Wee Peng Veeravalli, Venugopal V. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lau, Tze Siong Tay, Wee Peng Veeravalli, Venugopal V. |
| author_sort | Lau, Tze Siong |
| collection | NTU |
| description | The problem of quickest detection of a change in distribution is considered under the assumption that the prechange distribution is known, and the postchange distribution is only known to belong to a family of distributions distinguishable from a discretized version of the prechange distribution. A sequential change detection procedure is proposed that partitions the sample space into a finite number of bins and monitors the number of samples falling into each of these bins to detect the change. A test statistic that approximates the generalized likelihood ratio test is developed. It is shown that the proposed test statistic can be efficiently computed using a recursive update scheme, and a procedure for choosing the number of bins in the scheme is provided. Various asymptotic properties of the test statistic are derived to offer insights into its performance tradeoff between average detection delay and average run length to false alarm. Testing on synthetic and real data demonstrates that our approach is comparable or better in the performance to existing nonparametric change detection methods. |
| first_indexed | 2024-10-01T05:28:52Z |
| format | Journal Article |
| id | ntu-10356/137146 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:28:52Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1371462020-03-02T07:03:23Z A binning approach to quickest change detection with unknown post-change distribution Lau, Tze Siong Tay, Wee Peng Veeravalli, Venugopal V. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Quickest Change Detection Non-parametric Test The problem of quickest detection of a change in distribution is considered under the assumption that the prechange distribution is known, and the postchange distribution is only known to belong to a family of distributions distinguishable from a discretized version of the prechange distribution. A sequential change detection procedure is proposed that partitions the sample space into a finite number of bins and monitors the number of samples falling into each of these bins to detect the change. A test statistic that approximates the generalized likelihood ratio test is developed. It is shown that the proposed test statistic can be efficiently computed using a recursive update scheme, and a procedure for choosing the number of bins in the scheme is provided. Various asymptotic properties of the test statistic are derived to offer insights into its performance tradeoff between average detection delay and average run length to false alarm. Testing on synthetic and real data demonstrates that our approach is comparable or better in the performance to existing nonparametric change detection methods. MOE (Min. of Education, S’pore) Accepted version 2020-03-02T07:03:22Z 2020-03-02T07:03:22Z 2018 Journal Article Lau, T. S., Tay, W. P., & Veeravalli, V. V. (2019). A binning approach to quickest change detection with unknown post-change distribution. IEEE Transactions on Signal Processing, 67(3), 609-621. doi:10.1109/TSP.2018.2881666 1053-587X https://hdl.handle.net/10356/137146 10.1109/TSP.2018.2881666 2-s2.0-85056739268 3 67 609 621 en IEEE Transactions on Signal Processing © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSP.2018.2881666. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Quickest Change Detection Non-parametric Test Lau, Tze Siong Tay, Wee Peng Veeravalli, Venugopal V. A binning approach to quickest change detection with unknown post-change distribution |
| title | A binning approach to quickest change detection with unknown post-change distribution |
| title_full | A binning approach to quickest change detection with unknown post-change distribution |
| title_fullStr | A binning approach to quickest change detection with unknown post-change distribution |
| title_full_unstemmed | A binning approach to quickest change detection with unknown post-change distribution |
| title_short | A binning approach to quickest change detection with unknown post-change distribution |
| title_sort | binning approach to quickest change detection with unknown post change distribution |
| topic | Engineering::Electrical and electronic engineering Quickest Change Detection Non-parametric Test |
| url | https://hdl.handle.net/10356/137146 |
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